While there have been many years of research on the acoustoelectric effect, there has not been a successful approach or demonstration to providing a manufacturable, CW acoustoelectric amplifier with insertion gain. This paper will present results of a 169.5 MHz surface acoustic wave (SAW) delay line on 128YX lithium niobate with an embedded monolithic coupled acoustoelectric amplifier (CAEA) demonstrating a terminal gain of 1.2 dB. The impedance matched delay line without the CAEA has an insertion loss of 4.6 dB, and with the operational CAEA the measured gain is 1.2 dB, yielding a net insertion gain of 5.8 dB. The CAEA uses approximately 137 mW DC power at the current peak operational gain. The paper will present experimental results of device performance and discuss the new embodiment for achieving the net insertion gain.For transducer design it is essential to know the acoustic properties of the materials in their operating conditions. At frequencies over 15 MHz, standard methods are not well adapted because layers are very thin and backings have very high attenuation. In this paper we report on an original method for measuring the acoustic properties in the 15 to 25 MHz frequency range, corresponding to typical skin imaging applications, using a backing/piezoelectric multilayer structure. Onto a porous Pb(Zr0.53Ti0.47)O3 (PZT) substrate, a piezoelectric PZT-based layer with a thickness of ~ 20 μm was deposited and directly used to excite an acoustic signal into water. Here, the measured signal corresponds to the wave that is first reflected on a target in water, then propagates back to the multilayer structure, is transmitted through the thick film and further to the rear face of the porous backing, where it is again reflected and returns to the piezoelectric thick film, thus avoiding overlap with the electrical excitation signal. Two types of PZT backings with similar porosity of ~ 20 % and spherical pores with size of 1.5 μm and 10 μm were processed. The ultrasound group velocities were measured at ~ 3500 m/s for both samples. The acoustic attenuation of the backings with pore size of 1.5 μm and 10 μm were 12 dB/mm and 33 dB/mm, respectively, measured at 19 MHz. This advanced measuring technique demonstrated potential for simple measurements of acoustic properties of backing at high frequencies in operating conditions. Importantly, this method also enables rapid determination of the minimum required thickness of the backing in order to act as a semi-infinite medium, for high-frequency transducer applications.Accurate and automatic segmentation of medical images is a crucial step for clinical diagnosis and analysis. The convolutional neural network (CNN) approaches based on the U-shape structure have achieved remarkable performances in many different medical image segmentation tasks. However, the context information extraction capability of single stage is insufficient in this structure, due to the problems such as imbalanced class and blurred boundary. In this paper, we propose a novel Context Pyramid Fusion Network (named CPFNet) by combining two pyramidal modules to fuse global/multi-scale context information. Based on the U-shape structure, we first design multiple global pyramid guidance (GPG) modules between the encoder and the decoder, aiming at providing different levels of global context information for the decoder by reconstructing skip-connection. We further design a scale-aware pyramid fusion (SAPF) module to dynamically fuse multi-scale context information in high-level features. These two pyramidal modules can exploit and fuse rich context information progressively. Experimental results show that our proposed method is very competitive with other state-of-the-art methods on four different challenging tasks, including skin lesion segmentation, retinal linear lesion segmentation, multi-class segmentation of thoracic organs at risk and multi-class segmentation of retinal edema lesions.This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part. Our method does not require additional annotations of object parts or textures for supervision. Instead, we use the same training data as traditional CNNs. Our method automatically assigns each interpretable filter in a high conv-layer with an object part of a certain category during the learning process. Such explicit knowledge representations in conv-layers of CNN help people clarify the logic encoded in the CNN, i.e., answering what patterns the CNN extracts from an input image and uses for prediction. We have tested our method using different benchmark CNNs with various structures to demonstrate the broad applicability of our method. Experiments have shown that our interpretable filters are much more semantically meaningful than traditional filters.Iron deficiency (ID) is associated with sleep disorders, but standardized assessment of iron status in the diagnostic work-up and iron supplementation as treatment have not been considered in clinical practice. We investigated associations of ID with type and severity of sleep disorders and whether iron supplementation improves sleep-related symptoms. https://www.selleckchem.com/products/Dexamethasone.html In 2017, we conducted a scoping review for the period 1972-2016 using the terms "iron deficiency anemia" and "sleep" on biomedical database search engines, and in 2019, we updated our review with an ad-hoc search. Among the 93 articles meeting our inclusion criteria, 74/93 studies investigated restless legs syndrome (RLS), 8/93 periodic limb movements in sleep (PLMs), 3/93 sleep disordered breathing (SDB), 6/93 general sleep disturbances (GSD), and 2/93 attention-deficit/ hyperactivity disorder related sleep disorders (ADHD-SDs). A statistically supported positive association with ID was found in 22/42 RLS, 3/8 PLMs, 1/2 SDB, 3/4 GSD, and 1/2 ADHD-SDs association studies. The ad-hoc literature search revealed eight additional association studies with a statistically supported positive association in 2/5 RLS, 1/1 SDB, 1/1 ADHD-SDs, and 1/1 restless sleep disorder (RSD) studies. Iron supplementation was beneficial in 29/30 RLS (including five randomized controlled trials [RCTs]), 1/1 SDB, and 2/2 GSD treatment studies. Iron supplementation was also beneficial in 2/2 RLS (including two RCTs), 1/1 GSD (RCT), and 1/1 RSD studies identified in the ad-hoc search. In pediatric populations, 1/1 RLS, 1/1 SDB, 2/5 PLMs, 2/3 GSD and 1/2 ADHD-SDs studies found positive associations, and 6/6 RLS and 2/2 GSD studies demonstrated a benefit with iron supplementation. In conclusion, iron investigation and supplementation should be considered in patients presenting with sleep disorders. To investigate the role of ID in sleep in the future, a harmonization of study designs, including outcome measures and standardized iron and inflammation status is necessary.